In this work we utilize unsupervised domain adaptation for prostate lesion segmentation on VERDICT-MRI. Specifically, we use an image-to-image translation method to translate multiparametric-MRI data to the style of VERDICT-MRI. Given a successful translation we use the synthesized data to train a model for lesion segmentation on VERDICT-MRI. Our results show that this approach performs well on VERDICT-MRI despite the fact that it does not exploit any manual annotations.
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